When I left the academic world, by the end of 2015, I had almost finished a
review on the state of the art in usage of machine learning methods on
neuroscience: how predictive models can be used for certain diseases
(Alzheimer's, Parkinson's, etc) when the input data comes from structural (not
functional) magnetic resonance imaging.

As it turned out it was very difficult for me to actually finish it from outside
of academia, the now co-first author took the manuscript, made the necessary
changes to turn it from a mess into a readable sequence, and we started sending
it around to different journals... which is taking longer than expected. So we
have done what we should have done at the beginning of the entire process and we
uploaded it to arXiv.org. Here's the abstract:

In this paper, we provide an extensive overview of machine learning
techniques applied to structural magnetic resonance imaging (MRI) data to
obtain clinical classifiers. We specifically address practical problems
commonly encountered in the literature, with the aim of helping researchers
improve the application of these techniques in future works. Additionally, we
survey how these algorithms are applied to a wide range of diseases and
disorders (e.g. Alzheimer's disease (AD), Parkinson's disease (PD), autism,
multiple sclerosis, traumatic brain injury, etc.) in order to provide a
comprehensive view of the state of the art in different fields.